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Does anyone know how to calculate the error rate for a decision tree with R? I am using the rpart() function.
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The total error will be the sum of the individual errors, but out of the sum of all predictions. The error is 1-accuracy ( 1-0.8077 = 0.1923 ). To get the raw number, you can sum the off-diagonal elements from the confusion matrix ( 0+15 = 15 ).
To calculate the error rate for a decision tree in R, assuming the mean computing error rate on the sample used to fit the model, we can use printcp(). The Root node error is used to compute two measures of predictive performance, when considering values displayed in the rel error column and xerror column.
error (where error is the probability of making a mistake). There are two error rates to be considered: • training error (i.e. fraction of mistakes made on the training set) • testing error (i.e. fraction of mistakes made on the testing set) The error curves are as follows: tree size vs.
Error rate is calculated as the total number of two incorrect predictions (FN + FP) divided by the total number of a dataset (P + N).
Assuming you mean computing error rate on the sample used to fit the model, you can use printcp(). For example, using the on-line example,
> library(rpart) > fit <- rpart(Kyphosis ~ Age + Number + Start, data=kyphosis) > printcp(fit) Classification tree: rpart(formula = Kyphosis ~ Age + Number + Start, data = kyphosis) Variables actually used in tree construction: [1] Age Start Root node error: 17/81 = 0.20988 n= 81 CP nsplit rel error xerror xstd 1 0.176471 0 1.00000 1.00000 0.21559 2 0.019608 1 0.82353 0.82353 0.20018 3 0.010000 4 0.76471 0.82353 0.20018 The Root node error is used to compute two measures of predictive performance, when considering values displayed in the rel error and xerror column, and depending on the complexity parameter (first column):
0.76471 x 0.20988 = 0.1604973 (16.0%) is the resubstitution error rate (i.e., error rate computed on the training sample) -- this is roughly
class.pred <- table(predict(fit, type="class"), kyphosis$Kyphosis) 1-sum(diag(class.pred))/sum(class.pred) 0.82353 x 0.20988 = 0.1728425 (17.2%) is the cross-validated error rate (using 10-fold CV, see xval in rpart.control(); but see also xpred.rpart() and plotcp() which relies on this kind of measure). This measure is a more objective indicator of predictive accuracy.
Note that it is more or less in agreement with classification accuracy from tree:
> library(tree) > summary(tree(Kyphosis ~ Age + Number + Start, data=kyphosis)) Classification tree: tree(formula = Kyphosis ~ Age + Number + Start, data = kyphosis) Number of terminal nodes: 10 Residual mean deviance: 0.5809 = 41.24 / 71 Misclassification error rate: 0.1235 = 10 / 81 where Misclassification error rate is computed from the training sample.
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